An image may be segmented into regions. The designation of portions of an image to a particular segment is termed a segmentation mask. Machine learning models, such as neural networks, may be trained to generate a segmentation mask for an input image. For complex images or for data sets with limited training data, existing models may generate segmentation masks that may have poor quality relative to known segmentation for images. As one example, of such complex data with limited training data, three-dimensional medical imaging data segmented to designate abnormal tissue may be particularly challenging for automated systems to produce predicted segmentation that closely matches the identification of abnormal tissue by a medical professional. Improvements in automatic segmentation of such images (among other kinds) may improve medical outcomes and reduce delays of radiological procedures and interpretation.
The figures depict various embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein.